Lightweight approximate Nearest Neighbor algorithm library written in C++ (with Python/Go bindings).
N2 stands for two N's, which comes from 'Approximate N
earest N
eighbor Algorithm'.
Before N2, there has been other great approximate nearest neighbor libraries such as Annoy and NMSLIB. However, each of them had different strengths and weaknesses regarding usability, performance, and etc. So, N2 has been developed aiming to bring the strengths of existing aKNN libraries and supplement their weaknesses.
Metric | Definition | d(p, q) |
"angular" | 1 - cosθ | 1 - {sum(p i · q i) / sqrt(sum(p i · p i) · sum(q i · q i))} |
"L2" | squared L2 | sum{(p i - q i) 2} |
"dot" | dot product | sum(p i · q i) |
N2 supports three distance metrics. For "angular" and "L2", d (distance) is defined such that the closer the vectors are, the smaller d is. However for "dot", d is defined such that the closer the vectors are, the larger d is. You may be wondering why we defined and implemented "dot" metric as plain dot product and not as (1 - dot product). The rationale for this decision was to allow users to directly interpret the d value returned from Hnsw search function as a dot product value.
$ pip install n2
import numpy as np
from n2 import HnswIndex
N, dim = 10240, 20
samples = np.arange(N * dim).reshape(N, dim)
index = HnswIndex(dim)
for sample in samples:
index.add_data(sample)
index.build(m=5, n_threads=4)
print(index.search_by_id(0, 10))
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
Visit n2.readthedocs.io for full documentation. The documentation site explains the following contents in detail.
Author: kakao
Source Code: https://github.com/kakao/n2
License: Apache-2.0 license